CiroCattuto DominikBenz AndreasHotho GerdStumme Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems 2008 Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies. MirandaGrahl AndreasHotho GerdStumme Conceptual Clustering of Social Bookmark Sites Martin-Luther-Universität Halle-Wittenberg 50-54 2007 AndreasHotho GerdStumme Mining the World Wide Web – Methods, Ap- plications, and Perspectives Künstliche Intelligenz 5-8 2007 RobertJäschke AndreasHotho ChristophSchmitz BernhardGanter GerdStumme Discovering Shared Conceptualizations in Folksonomies Journal of Web Semantics 6 38-53 2008 RobertJäschke BeateKrause AndreasHotho GerdStumme Logsonomy — A Search Engine Folksonomy AAAI Press 2008 In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us. BeateKrause AndreasHotho GerdStumme The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems 2008 BeateKrause AndreasHotho GerdStumme A Comparison of Social Bookmarking with Traditional Search 101-113 2008 Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system. C.Schmitz S.Staab R.Studer G.Stummen J.Tane Accessing Distributed Learning Repositories through a Courseware Watchdog AACE 909-915 2002 GerdStumme A Finite State Model for On-Line Analytical Processing in Triadic Contexts. Springer 3403 315-328 2005 Themenheft Web Mining Künstliche Intelligenz 5-8 2007